Most previous studies are focused on encoder-decoder models on audio captioning. AAC is presented with an encoder-decoder model in [1]}. This architecture contains an encoder with three Bi-directional GRU layers and a decoder with two GRU layers with an attention mechanism between encoder and decoder. [2]} uses the same encoder-decoder architecture to propose the compilation of the Clotho dataset. In [3]}, [4]}, the researchers present a new audio captioning dataset, AudioCaps, and they propose a new encoder-decoder architecture with semantic alignment. Another model is proposed in [5]} by using a five layer convolutional recurrent neural network (CRNN) in the encoder and one layer GRU in the decoder. Reinforcement learning is also added to this model. In [3]}, audio and semantic embeddings are combined to improve captioning performance. The subjects and verbs in the captions are extracted in order to create semantic embeddings. These embeddings are used to feed encoder-decoder architecture with acoustic features.
